Cohort Protocol: Risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling

Xianghui Huang¹Cuimin Su²Ying Lin¹Tianyi Zhou³Ruming Ye¹Dan Li¹Miaoshuang Liu⁴Guanhong Wu⁴Wanting Li⁴Namei Xie⁴Xiaofang Deng⁴Nanxi Zhu⁴Shaohong Lin⁴Qin Li^3*Kai Yan^5*Deyi Zhuang^1*

• ¹Fujian key Laboratory of Neonatal Diseases, Xiamen, Fujian Province, China
• ²Jinjiang Municipal Hospital, Quanzhou, Fujian Province, China
• ³Department of Maternal and Child Health, School of Public Health, Health Science Centre, Peking University, Beijing, Beijing Municipality, China
• ⁴Children's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, China
• ⁵Shanghai Children's Medical Center, Shanghai, Shanghai Municipality, China

IntroductionInfancy and early childhood are the key stage for the rapid development of brain structure and function, and brain development at this stage has a profound impact on the future intelligence, behavior and health of individuals. A growing body of research suggests that maternal inflammation, as a potential environmental factor, may affect brain development in infants and young children through a variety of mechanisms. Therefore, it is of great significance to evaluate the risk of maternal inflammation to early brain development in infants and young children in infants and young children based on multi-source data modeling to understand the mechanism of early development and prevent brain development disorders.Methods and AnalysisBetween December 2021 and May 2024, 360 pairs of pregnant women and their offspring were recruited into the Xiamen Children's Brain Development Cohort. Pregnant women’s exposure during pregnancy was collected through standardized and structured questionnaires and medical records. All children were followed up to 3 years of age. Questionnaires, behavioral assessments, and imaging methods were used, and the infants and young children's environmental exposures were collected, and the environmental exposures of infants and young children were collected, and the cognitive, emotional, and language development of children was investigated, and blood samples were collected for whole exome sequencing and exposure - related sequencing.ConclusionIn this study, we used deep learning artificial intelligence to construct an early risk assessment model for infant brain development based on the developmental trajectory and developmental results of early brain structure, function, and connections under the complex interaction of "gene-image-environment-behavior" multi-factors, which can improve the early identification and precise intervention of problems in this period, and improve infants cognitive learning and work performance in childhood, adolescence and even adulthood.